Maximum Likelihood Phylogenetic Reconstruction From High-Resolution Whole-Genome Data and a Tree of 68 Eukaryotes

Abstract

The rapid accumulation of whole-genome data has renewed interest in the study of the evolution of genomic architecture, under such events as rearrangements, duplications, losses. Comparative genomics, evolutionary biology, and cancer research all require tools to elucidate the mechanisms, history, and consequences of those evolutionary events, while phylogenetics could use whole-genome data to enhance its picture of the Tree of Life. Current approaches in the area of phylogenetic analysis are limited to very small collections of closely related genomes using low-resolution data (typically a few hundred syntenic blocks); moreover, these approaches typically do not include duplication and loss events. We describe a maximum likelihood (ML) approach for phylogenetic analysis that takes into account genome rearrangements as well as duplications, insertions, and losses. Our approach can handle high-resolution genomes (with 40,000 or more markers) and can use in the same analysis genomes with very different numbers of markers. Because our approach uses a standard ML reconstruction program (RAxML), it scales up to large trees. We present the results of extensive testing on both simulated and real data showing that our approach returns very accurate results very quickly. In particular, we analyze a dataset of 68 high-resolution eukaryotic genomes, with from 3,000 to 42,000 genes, from the eGOB database; the analysis, including bootstrapping, takes just 3 hours on a desktop system and returns a tree in agreement with all well supported branches, while also suggesting resolutions for some disputed placements.

Reconstructing Ancestral Genomic Orders Using Binary Encoding and Probabilistic Models

“…-InferCARsPro uses a neutral model accounting for all changes of adjacencies, however biased model for phylogeny reconstruction has been successfully applied for genome rearrangement scenarios [11]. -The total number of states for each gene is exactly equal to 2×n−2 where n is the number of genes.…”

“…A gene order can be expressed as a sequence of adjacency information that specifies presence or absence of all the adjacencies [10,11]. Denote the head of a gene i by i h and its tail by i t .…”

“…Severl models have been proposed to probabilistically characterize the changes of gene adjacencies by common types of rearrangement operations such as inversion, transposition as well as DCJ [7,11]. In this study, we use the model that has been successfully applied for phylogeny reconstruction in the context of genome rearrangement as suggested in [11]. In particular, every DCJ operation breaks two random adjacencies uniformly chosen from the gene-order string and subsequently creates two new ones.…”

“…In order to carry out a complete evaluation over a group of methods under a wide range of configurations, we conducted a collection of simulation experiments following the standard steps of such tests that have been extensively adopted [17,11].…”

Duplication, Rearrangement and Reconciliation: A Follow-Up 13 Years Later

“…If they lie in a set intersection, they integrate several kinds of mutations. Apart from the red area, the names aside the dots, squares and stars are examples of softwares or methods achieving the described goal (PhyML [2], Count [3], ODT [4], PhylDog [5], Exemplar [6], Pathgroup [7], Grappa [8],Code [9]). They are often chosen among a lot of other examples which would have been as relevant.…”